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All Journal Jurnal Dinamika Pengabdian (JDP) Chimica et Natura Acta FaST : Jurnal Sains dan Teknologi EIGEN MATHEMATICS JOURNAL Journal of Islamic Civilization Jurnal Ilmiah Kesehatan Iqra International Journal of Engineering, Science and Information Technology Bioinformatics and Biomedical Research Journal Cermin Dunia Kedokteran Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan JKKI : Jurnal Kedokteran dan Kesehatan Indonesia QiST: Journal of Quran and Tafseer Studies Makara Journal of Health Research MEDICINUS Public Health and Safety International Journal (PHASIJ) Jurnal Al Burhan Jurnal Multidisiplin West Science Sahabat Sosial: Jurnal Pengabdian Masyarakat West Science Interdisciplinary Studies International Journal of Health Sciences West Science Interdisciplinary Studies Multicultural Islamic Education Review West Science Social and Humanities Studies Journal of World Future Medicine, Health and Nursing The Eastasouth Journal of Social Science and Humanities JIMAD : Jurnal Ilmiah Multidisiplin Educompassion: Jurnal Integrasi Pendidikan Islam Dan Global Journal of Tecnologia Quantica Journal of Biomedical and Techno Nanomaterials
Dito Anurogo, Dito
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Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Identifikasi Jamur Endofit Pada Tanaman Obat Tradisional Di Sulawesi Selatan Anurogo, Dito; Rahmat, Rezqiqah Aulia; Pannyiwi, Rahmat
JIMAD : Jurnal Ilmiah Multidisiplin Vol. 3 No. 2 (2026): JIMAD : Jurnal Ilmiah Multidisiplin (January)
Publisher : Asosiasi Guru dan Dosen Seluruh Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59585/jimad.v3i1.862

Abstract

Endophytic fungi are microorganisms that live within plant tissues without causing disease symptoms. This study aimed to identify endophytic fungi associated with traditional medicinal plants in South Sulawesi that may produce bioactive compounds. The research was exploratory in nature, isolating fungi from leaves and stems using Potato Dextrose Agar (PDA) medium, and identifying them based on macroscopic and microscopic morphological characteristics. The results revealed the presence of several genera of endophytic fungi, including Aspergillus, Penicillium, Fusarium, and Trichoderma. These findings indicate the significant potential of endophytic fungi in the development of biotechnology-based traditional medicines.
Kajian Prediksi 3-Dimensi Biomarker Kanker Payudara Dari Jalur Ekspresi LincRNA-ROR/MIR-145/ARF6 [3D Prediction of Breast Cancer Biomarker from The Expression Pathway of LincRNA-ROR/MIR-145/ARF6] Parikesit, Arli Aditya; Anurogo, Dito
FaST - Jurnal Sains dan Teknologi (Journal of Science and Technology) Vol. 1 No. 2 (2018): MAY
Publisher : Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

According to WHO, breast cancer is one of the main causes of mortality in women. To overcome this malady, molecular biomedical research is carried out intensively. Anomalies in the lincRNA-RoR/miR-145/ARF6 expression pathway were found to play a very important role in breast cancer, especially in the type of Triple-Negative Breast Cancer (TNBC), which is the most dangerous variant of the deadly disease. Bioinformatics research has found the existence of non-coding RNA (ncRNA) in these expression pathways, whose interactions are worth studying with 3-dimensional prediction methods. The 3-D prediction method for biomolecules has been widely developed and has been successfully applied to DNA and protein. However, for the structure of RNA, it has just been developed, due to its low stability and very high dynamics on the biomolecule. Our aim is to apply the latest computational method for predicting the 3-dimensional structure of ncRNA, which can be applied as key information in biomedical application research. Extrapolation of kinetics and thermodynamic indicators of ncRNA ultimately yields the siRNA Linc-ROR 3-Dimensional structure and siRNA mRNA-ARF6, each having 13 and 8 hydrogen bonds. The existence of these hydrogen bonds is very important in maintaining the stability of the compounds and shows its efficacy as drug candidates. It is expected that preliminary information from the predicted 3-dimensional structure of ncRNA is useful for optimization of laboratory experiments in the field of crystallographic biomolecules.
EVALUATE THE EFFECTIVENESS OF RNAI-BASED NANOPARTICLES AS THERAPY FOR PANCREATIC CANCER Anurogo, Dito; Krit, Pong; Lek, Siri
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i1.2019

Abstract

Pancreatic cancer is one of the most lethal cancers with limited effective treatment options. RNA interference (RNAi) offers a promising therapeutic approach, but efficient delivery systems are essential. To evaluate the effectiveness of RNAi-based nanoparticles as a therapy for pancreatic cancer, focusing on tumor inhibition and cell viability. A comprehensive study combining in vitro, in vivo, and clinical approaches was conducted. Pancreatic cancer cell lines (PANC-1, BxPC-3, AsPC-1) and mouse models with human pancreatic tumors were treated with RNAi-based nanoparticles. Characterization of nanoparticles included size, charge, and stability assessments using DLS and HPLC. RNAi-based nanoparticles inhibited tumor growth by 70% in mouse models and reduced cell viability by 60% in vitro. Nanoparticles demonstrated high stability and effective internalization into cancer cells, leading to significant gene silencing and apoptotic effects. RNAi-based nanoparticles show significant potential as an effective therapy for pancreatic cancer, demonstrating substantial tumor inhibition and cell viability reduction. Further clinical trials are necessary to confirm these findings and optimize nanoparticle formulations.
ARTIFICIAL INTELLIGENCE IN MEDICINE: A DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK FOR PATHOLOGICAL IMAGE ANALYSIS AND CANCER GRADING Smith, James; Harris, Oliver; Anurogo, Dito
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i4.2480

Abstract

The histopathological analysis of tissue slides is the gold standard for cancer diagnosis and grading. However, this process is labor-intensive, time-consuming, and prone to inter-observer variability, which can affect clinical outcomes. The advent of artificial intelligence (AI), particularly deep learning, presents a transformative opportunity to enhance diagnostic precision and efficiency in pathology. This study aimed to develop, train, and validate a deep learning convolutional neural network (CNN) for the automated analysis of pathological images to accurately classify malignancies and provide reliable cancer grading. A robust CNN model was trained on a comprehensive, curated dataset of thousands of annotated digital histopathology slides from multiple cancer types. The model’s performance was rigorously evaluated against the consensus diagnoses of expert pathologists using key metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). Our developed CNN model demonstrated exceptional performance, achieving an overall accuracy of 98.7% in distinguishing malignant from benign tissues. For cancer grading, the model yielded a Cohen’s Kappa score of 0.92, indicating almost perfect agreement with expert pathologists. The model also showed high robustness to variations in staining and image acquisition protocols. This research confirms that a deep learning CNN can function as a highly accurate and reliable tool for automated pathological image analysis and cancer grading. Integrating such AI systems into clinical workflows could significantly augment the capabilities of pathologists, leading to improved diagnostic consistency, reduced workload, and ultimately, better patient care.
Quantum Machine Learning for Drug Discovery: Accelerating the Simulation of Molecular Hamiltonians on Noisy Intermediate-Scale Quantum (NISQ) Devices Santos, Luis; Reyes, Maria Clara; Gonzales, Samantha; Anurogo, Dito
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i5.2796

Abstract

Drug discovery increasingly relies on accurate simulation of molecular Hamiltonians, yet classical computational methods face exponential scaling barriers when modeling complex quantum systems. Recent advances in quantum machine learning (QML) and the availability of Noisy Intermediate-Scale Quantum (NISQ) devices offer new opportunities to accelerate molecular simulation despite hardware noise and qubit limitations. This study aims to evaluate the effectiveness of QML-based variational algorithms in improving the efficiency and accuracy of Hamiltonian simulation for drug-relevant molecules on NISQ platforms. A hybrid quantum–classical methodology was employed, combining variational quantum eigensolvers, noise-aware circuit optimization, and supervised learning models trained to predict energy landscapes. Experimental simulations were performed using IBM-Q and Rigetti NISQ architectures, supported by classical benchmarks for validation. The results demonstrate that QML-enhanced variational circuits significantly reduce computational depth while maintaining competitive accuracy compared to classical methods, particularly for medium-sized molecular systems. The findings also reveal that noise-adaptive training improves algorithm robustness, enabling more reliable energy estimation under realistic quantum noise conditions. The study concludes that QML provides a promising pathway for accelerating early-stage drug discovery by enabling efficient molecular Hamiltonian simulation on current-generation quantum hardware. Further integration of error mitigation and scalable QML frameworks will be essential for future advancements.
Nanobubbles for Precision Oncology Zumratul Rabbani, Khadijah; Dwi Laksono, Pudjo Dwi Laksono; Anurogo, Dito
MEDICINUS Vol. 39 No. 1 (2026): MEDICINUS
Publisher : PT Dexa Medica

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56951/gsz6t860

Abstract

Nanobubbles (NBs) represent a unique class of sub-200 nm carriers that integrate deep tissue penetration with ultrasound (US)-responsive functionality, offering opportunities for simultaneous imaging, oxygenation, and therapeutic delivery in solid tumors. This review synthesizes the physicochemical principles governing NB stability with translational designconsiderations, including interfacial charge, free-lipid content, bubble spacing, and zeta (ζ)-potential as determinants of uptake and cytotoxicity. Particular emphasis is placed on gas-based payloads: oxygen nanobubbles for alleviating tumor hypoxia and carbon monoxide-releasing molecules (CO-RMs), nitric oxide (NO), and hydrogen sulfide (H₂S) for redoximmunometabolicmodulation within hormetic dose windows. Preclinical data demonstrate that oxygen nanobubbles enhance radiotherapy and chemotherapy responses by reversing hypoxia-induced resistance, while CO, NO, and H₂Sdonors—delivered in biphasic, dose-sensitive ranges—enable immunomodulation and reprogramming of the tumor microenvironment. We further distill case-level evidence (e.g., IR780–docetaxel nanobubbles in pancreatic cancer) intopractical design rules and discuss engineering levers such as shell composition, crosslinking chemistry, and acoustic parameterization. Finally, this review outlines translational roadmaps covering scalable manufacturing, imaging-guideddosimetry, and early-phase clinical strategies. Collectively, nanobubble-based, gas-augmented, ultrasound (US)-triggered systems represent an emerging precision platform with the potential to transition from experimental prototypes towardcontrolled clinical evaluation in oncology.
A 3D-PRINTED, GRAPHENE-REINFORCED HYDROGEL SCAFFOLD FOR ENHANCED OSTEOGENIC DIFFERENTIATION OF MESENCHYMAL STEM CELLS Anurogo, Dito
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i5.2761

Abstract

Bone tissue engineering requires scaffolds that replicate the mechanical stiffness and electroactive properties of native bone, features that conventional hydrogels lack. This study aimed to design, fabricate, and validate a 3D-printed graphene-reinforced hydrogel scaffold that enhances osteogenic differentiation of human mesenchymal stem cells (hMSCs) via combined mechanical and electrical stimulation. A composite bio-ink was developed by incorporating graphene nanoparticles (0, 0.1, 0.2, and 0.5% w/v) into a biocompatible hydrogel matrix, optimized for extrusion-based 3D printing. Scaffolds with a controlled pore size of 300 ?m were fabricated and analyzed for compressive strength, degradation kinetics, and electrical conductivity using a four-point probe. hMSCs were seeded onto the scaffolds and cultured under osteogenic conditions for 28 days. Osteogenic differentiation was assessed by alkaline phosphatase (ALP) activity (day 14), qPCR for RUNX2 and osteocalcin (OCN) (day 21), and Alizarin Red S staining for mineralization (day 28). Data were analyzed using ANOVA and regression modeling. The 0.2% w/v graphene-reinforced scaffolds showed optimal performance, with compressive strength of 35.0 MPa and electrical conductivity of 0.15 S/m, significantly higher than pure hydrogel controls. hMSCs cultured on these scaffolds exhibited increased ALP activity, upregulation of RUNX2 and OCN, and enhanced mineralization. At 0.5% w/v graphene, excessive viscosity hindered printability and reduced cell viability. Overall, the 3D-printed graphene-reinforced hydrogel scaffold at 0.2% w/v creates a synergistic electromechanical microenvironment, robustly promoting hMSC osteogenesis, and offers a scalable platform for next-generation bone tissue engineering.
Determinasi Faktor yang Mempengaruhi Keberhasilan Inisiasi Menyusu Dini (IMD) pada Persalinan Normal: Studi Simulasi Berbasis Data Sintetis Anurogo, Dito; Laksmi, Noorhani Dyani
Public Health and Safety International Journal Vol. 6 No. 01 (2026): Public Health and Safety International Journal (PHASIJ)
Publisher : YCMM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55642/phasij.v6i01.1298

Abstract

Inisiasi Menyusu Dini (IMD) dilaporkan berasosiasi dengan penurunan kematian neonatal hingga 22% dan dipandang sebagai pintu masuk bagi keberhasilan ASI eksklusif. Cakupan IMD nasional 58,2% masih di bawah target Renstra 66%. Tujuan: Menganalisis faktor yang mempengaruhi keberhasilan IMD pada ibu bersalin normal dan menguji peran pengetahuan ibu sebagai mediator pada hubungan dukungan petugas kesehatan dengan keberhasilan IMD. Metode: Studi simulasi berbasis data sintetis yang membangkitkan 150 observasi ibu bersalin normal menggunakan distribusi parameter yang dikalibrasi dari literatur SDKI 2022 dan studi-studi cross-sectional terdahulu di Indonesia. Pembangkitan data dilakukan dengan R versi 4.3.1 (paket simstudy dan MASS), benih acak (random seed) ditetapkan untuk reproduksibilitas. Analisis meliputi chi-square, regresi logistik ganda dengan pemeriksaan multikolinearitas (VIF), dan uji mediasi bootstrap 5.000 sampel. Hasil: Keberhasilan IMD 61,3% (n=92). Faktor dominan: pengetahuan ibu (aOR=5,84; 95% CI: 2,28–14,95; p<0,001), dukungan petugas kesehatan (aOR=3,42; p=0,002), dukungan keluarga (aOR=2,89; p=0,008), pendidikan SMA ke atas (aOR=2,15; p=0,044), dan paritas multipara (aOR=1,98; p=0,047). Pengetahuan ibu memediasi 30,1% pengaruh dukungan petugas terhadap keberhasilan IMD. Simpulan: Dalam skenario simulasi, pengetahuan ibu muncul sebagai determinan utama sekaligus jalur mediasi penting. Kerangka analisis ini dapat digunakan sebagai dasar perencanaan studi empiris lanjutan dan perhitungan kekuatan statistik untuk evaluasi program edukasi prenatal terpadu di fasilitas kesehatan primer.
Co-Authors Abdul Rahim Abdul Rahman Rahim Adhy Firdaus Agus Gunawan Ainun Jariyah Albertus Ata Maran Albina Bare Telan, Albina Bare Amansyah, Farid Ami Febriza Andari, Soetji Andarmoyo, Sulistyo Anis Fauzi, Anis Arda, Darmi Ardi, Abdullah Aripa, Lusyana Arli Aditya Parikesit Asbath Said Auliah, Rezki Awaliah, Nur Rahmah Bahjah, Dzata Bambang Winardi Bansaleng, Yessikah F. Borut, Mohamad Budhy Munawar Rachman Buka, Sisilia Prima Yanuaria Dadang Muhammad Hasyim Dewantara, Muhammad Iqbal Dwi Laksono, Pudjo Dwi Laksono Ekawati, Nur Eko Prastyo Eni Kurniati, Eni Erlinawati, Noor Diah Ernita Sari Fachry Abda El Rahman Farooq Mujahid, Muhammad Umer Farzani, Dwi Andina Fatany, Alief Ihram Fitria, Anni Fitriani, Fenny Gangga Anuraga Gonzales, Samantha Guilin, Xie Habibi Habibi Hani Brilianti Rochmanto Hardin La Ramba Harfika, Meiana Harmanto Harmanto Harris, Oliver Hasibuan, Evis Ritawani Haulussy, Rais Rahman Heni Widyaningsih, Heni Herlambang Prehananto, Herlambang Hermansyah Hermansyah Hidayati, Nanda Hijrah Hijrah Hiola, Siti Fatmah Ibad, M. Nashoihul Ibrahim, Juliani Idris Idris Ikrar, aruna Ikrar, Taruna Ilmi, Ahmad Alfarobi Jauharul Indra, Indra Intes, Amina Iwan Harsono Jia Yi Wang (王 家儀) , Jia Yi Wang Joko Sangaji Judijanto, Loso Junaedi, J Juwariyah, Siti K, Fitriani. Kao, Tzu-Jen Karimah, Annisa Ayu Kasmiati Kasmiati Kerwanto Khairunnisa Khairunnisa Krit, Pong Kurniawati Kurniawati Laksmi, Noorhani Dyani Latif, Sarifudin Andi Lek, Siri Leli, Leli lidia fitri, lidia Mahendika, Devin Mappanyompa, Fatimah Mardikawati, Budi MAURITZ PANDAPOTAN MARPAUNG Mohamad Firdaus Mudrika Mudrika Muhammad Azhar Irwansyah Muhammad Hanif Muhtadi, Muhamad Ammar Mulyadi, Amelia Astrid Muntasir, Muntasir Musiana, Musiana Muslimin B, Muslimin B Nabila Diyana Putri Nelly Nelly ningrum, dedah - Nunung Suryana Jamin Nur Faidah, Nur Nur Hasanah Nuralfin Anripa Nurkhalika, Rachmi Nurmila, Nurmila Nursiah, Andi Nursinah, A. Pannyiwi, Rahmat Parikesit, Arli A Puspita, Kori R. Rusli Rachman , Budhy Munawar Rahagia, Rasi Rahmat, Rezqiqah Aulia Reyes, Maria Clara Rieuwpassa, Sarmalina Rina Inda Sari Rini Nuraini, Rini Rini Susanti Rosdiana Rosdiana Saad, Rahmiyani Sahabuddin, Rosdiana Sakriawati, Sakriawati Salsabila, Anindya Samsani, Samsani Santos, Luis Saprudin, Udin Sardi Anto Sasmita, Niken Sasadhara Sembiring, Rinawati Sipahutar, Pongki Siti Aisyah Smith, James Sobihah, Hani Solehudin, Solehudin Sompa, Andi Weri Srifitayani, Nur Rahma Suarni, Agusdiwana Suat, Hatty Suat, Rahmawati Sulaeman Sulaeman SULASMI ANGGO Sulfiani, Sulfiani Sumarauw, Jeane L.I. Sumerli A, Chevi Herli Syahrir, Andi Karlina Syarif, Abdillah Syarif, Ubed Abdilah Tyas Putri Utami Ulfah Mahardika Pramono Putri Utami, Dia Rejeki Verawati Verawati Wahidin Wahidin Wilanda, Alifah Ximenis, Virgolie Diknas Yakobus, I Ketut Yamtana Yamtana, Yamtana Yanik, Carri Noer Fida Yermi, Yermi Yusraa, Yusraa Zani, Benny Novico Zohriah, Anis Zulham Zulham Zulhan Widya Baskara Zumratul Rabbani, Khadijah